Farinaz Koushanfar
About Farinaz Koushanfar
Farinaz Koushanfar is a Research Scientist and Professor of Electrical and Computer Engineering at the University of California, San Diego. She specializes in automated holistic cross-layer co-design and optimization of learning algorithms, security, and privacy-preserving computing.
Current Role at Chainlink Labs
Farinaz Koushanfar serves as a Research Scientist at Chainlink Labs, a position she has held since 2020. In this role, she focuses on advancing research initiatives that align with the company's objectives in blockchain technology and decentralized systems. Her expertise contributes to the development of innovative solutions that enhance the security and efficiency of smart contracts and decentralized applications.
Academic Background and Education
Farinaz Koushanfar completed her Doctor of Philosophy (PhD) in Electrical Engineering and Computer Science (EECS) at the University of California, Berkeley, from 2001 to 2005. She also studied at Sharif University of Technology and the University of California, Los Angeles, further solidifying her foundation in electrical and computer engineering.
Professional Experience at UC San Diego
Since 2015, Koushanfar has been a Professor and Henry Booker Faculty Scholar of Electrical and Computer Engineering at the University of California, San Diego. Her tenure at UC San Diego has been marked by her role as the founding co-director of the UCSD Center for Machine Intelligence, Computing, and Security (MICS), where she leads initiatives in machine intelligence and security research.
Previous Academic Role at Rice University
Prior to her current position, Farinaz Koushanfar was a Professor of Electrical and Computer Engineering at Rice University from 2006 to 2015. During her nine years in Houston, Texas, she contributed to the academic community through teaching and research in various aspects of electrical and computer engineering.
Research Focus and Areas of Expertise
Koushanfar's research is centered on automated holistic cross-layer co-design and optimization of learning algorithms, with a particular emphasis on security and privacy-preserving computing. Her work includes secure multi-party computation, addressing critical challenges in the intersection of machine learning and data security.